Configurable output data formats

ABSTRACT

Configurable core domains of a speech processing system are described. A core domain output data format for a given command is originally configured with default content portions. When a user indicates additional content should be output for the command, the speech processing system creates a new output data format for the core domain. The new output data format is user specific and includes both default content portions as well as user preferred content portions.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation of, and claims the benefit ofpriority of, U.S. Non-Provisional patent application Ser. No.16/569,780, filed Sep. 13, 2019 and titled “CONFIGURABLE OUTPUT DATAFORMATS”, which is a continuation of U.S. Non-Provisional patentapplication Ser. No. 15/611,228, filed Jun. 1, 2017 and titled“CONFIGURABLE OUTPUT DATA FORMATS”, issued as U.S. Pat. No. 10,418,003.The contents of the above are hereby expressly incorporated herein byreference in their entireties.

BACKGROUND

Speech recognition systems have progressed to the point where humans caninteract with computing devices using their voices. Such systems employtechniques to identify the words spoken by a human user based on thevarious qualities of a received audio input. Speech recognition combinedwith natural language understanding processing techniques enablespeech-based user control of a computing device to perform tasks basedon the user's spoken commands. The combination of speech recognition andnatural language understanding processing techniques is referred toherein as speech processing. Speech processing may also involveconverting a user's speech into text data which may then be provided tovarious text-based software applications.

Speech processing may be used by computers, hand-held devices, telephonecomputer systems, kiosks, and a wide variety of other devices to improvehuman-computer interactions.

BRIEF DESCRIPTION OF DRAWINGS

For a more complete understanding of the present disclosure, referenceis now made to the following description taken in conjunction with theaccompanying drawings.

FIG. 1 illustrates a speech processing system that configures coredomain output data formats based on user preferences according toembodiments of the present disclosure.

FIG. 2 is a diagram of components of a speech processing systemaccording to embodiments of the present disclosure.

FIG. 3 is a conceptual diagram of how text-to-speech processing isperformed according to embodiments of the present disclosure.

FIG. 4 illustrates data stored and associated with user profilesaccording to embodiments of the present disclosure.

FIG. 5A illustrates a default weather core domain output data formataccording to embodiments of the present disclosure.

FIG. 5B illustrates an altered weather core domain output data formataccording to embodiments of the present disclosure.

FIGS. 6A through 6C are a signal flow diagram illustrating theprocessing of a user command and the generation of responsive contentusing configurable output data formats according to embodiments of thepresent disclosure.

FIGS. 7A through 7C are a signal flow diagram illustrating theprocessing of a user command and the generation of responsive contentusing configurable output data formats according to embodiments of thepresent disclosure.

FIG. 8 illustrates an output data format associated with domain specificslot data according to embodiments of the present disclosure.

FIG. 9 is a block diagram conceptually illustrating example componentsof a device according to embodiments of the present disclosure.

FIG. 10 is a block diagram conceptually illustrating example componentsof a server according to embodiments of the present disclosure.

FIG. 11 illustrates an example of a computer network for use with thesystem.

DETAILED DESCRIPTION

Automatic speech recognition (ASR) is a field of computer science,artificial intelligence, and linguistics concerned with transformingaudio data associated with speech into text data representative of thatspeech. Similarly, natural language understanding (NLU) is a field ofcomputer science, artificial intelligence, and linguistics concernedwith enabling computers to derive meaning from text input containingnatural language. Likewise, text-to-speech (TTS) is a field of computerscience, artificial intelligence, and linguistics concerned withenabling computers to output synthesized speech. ASR, NLU, and TTS maybe used together as part of a speech processing system.

A speech processing system may be configured to execute one or morecommands corresponding to input speech. The speech processing system maybe capable of implementing commands in a variety of subject areas (alsoreferred to as domains) such as weather, shopping, music, videos, etc.for which a user may request content. For example, a user may say“what's the weather,” and the speech processing system may respond withthe current temperature of a location of the user, as determined basedon a location of a user device that captured the user's utterance.

Certain domains may be controlled by the same company or entity thatoperates the ASR, NLU or other operations. Such domains may be referredto as core domains. Other domains may be operated by third parties andmay expand the capabilities of the speech processing beyond the coredomain functionality offered by the speech processing system. Domain, asused herein, may refer to a category of content, such as music, videos,weather, etc. For example, a pizza delivery store may provideinformation that the speech processing system can use to analyzeincoming requests for pizza delivery from the particular pizza deliverystore. An incoming utterance may thus be processed using ASR and NLU toobtain data that can be sent to the pizza delivery store to ultimatelycause a pizza (or other order) to be delivered to the appropriate user.Similarly, a third party music service may be configured to operate withthe system so that a user can request music from the third party musicservice, the system can understand the request is for that service andcan pass a request to the service, and the service can work with thesystem to output music to the requesting user's device. Such additionaldomains may be referred to as add-on or third party domains. While coredomains may be active for a particular user by default, add-on domainsmay need to be authorized by a user in order to be used to process arequest from the user.

Each core domain may be configured to output default content in responseto a spoken utterance. For example, under default conditions, a requestfor weather information from a weather core domain may result in theweather domain outputting information about the temperature.

In various situations, a user may desire to know additional contentother than the default content for a particular domain. For example,while the weather core domain is configured to output the temperature inresponse to every input request, a user may desire to also know thewindspeed and the air quality of the user's location. In suchsituations, the user may separately have to ask the system “what is theweather,” “what is the air quality index,” and “what's the wind speed.”Alternatively, if the desired information is unavailable from a coredomain, the user may have to form a request directed to an add-on domainto obtain the required information. For example, a user may have tocreate an utterance such as “ask Pilot Assistant what's the wind speedtoday” to obtain wind-speed information. Requiring the user to speakmultiple utterances to the system may be undesirable because it providesa segmented user experience that is time consuming in terms of speakingthe utterances as well as receiving the content.

The present disclosure allows for a more streamlined user experience byconfiguring core domains of a speech processing system with configurablecontent portions (e.g., plugins or slots) that may relate to otherinformation from core domains or information from add-on domains. Suchconfigurable portions enable a user to customize content output by adomain. For example, a default output data format for a weather coredomain may be configured to simply output the temperature to a user. Thesystem, based on user preferences, may add portions to the defaultoutput data format for the weather core domain that cause the system tooutput the temperature as well as air quality index and wind speedwhenever the user asks the system “what's the weather,” or the like. Ifthe air quality index and/or wind speed are unavailable to the weathercore domain, the system may be configured to obtain that informationfrom an alternate service, such as an application server or other datastore associated with an add-on domain. The user may configure thesystem to include desired add-on domains for a given utterance byselecting such from a list of add-on domains. For example, a user maylogin to a user profile, and configure which domains (i.e., core and/oradd-on) that are invoked by a given user command.

The system may then create customized output data that includes certaindata from one domain (e.g., the temperature information from the coreweather domain) and certain data from another domain (e.g., the airquality index and wind speed information from the Pilot Assistantdomain). The system may also be configured to store the user'spreferences with regard to the customized output data such that if theuser repeats the request that should be customized (e.g., “what's theweather”) the system will recognize that the request calls forcustomized output data and will obtain the data from the multipledomains for eventual return to the user (for example, by performing TTSon the customized output data to create a speech output that will besent to the user's device).

As noted above, the speech processing system may be configured toreceive content pertaining to the configurable portions from one or morethird party content sources (e.g., add-on domains or other sources notcontrolled or maintained by the speech processing system). For example,if the speech processing system does not store air quality indexinformation and/or wind speed information, the speech processing systemmay link the provider of such information (e.g., the Pilot Assistantsource) to a weather request made by the particular user. The teachingsof the present disclosure provide a smooth user experience that allows auser to obtain all desired content related to a request to a certaindomain (even if the desired content is obtained from another domain) bysimply speaking a single utterance or issuing a single command.

FIG. 1 shows a speech processing system 100 capable of configuringdomain frameworks according to user preferences as described herein.Although the figures and discussion illustrate certain operational stepsof the system 100 in a particular order, the steps described may beperformed in a different order (as well as certain steps removed oradded) without departing from the intent of the disclosure. As shown inFIG. 1 , the system 100 may include one or more speech-controlleddevices 110 local to a user 5, as well as one or more networks 199 andone or more servers 120 connected to the speech-controlled device(s) 110across the network(s) 199. The server(s) 120 (which may be one or moredifferent physical devices) may be capable of performing traditionalspeech processing (e.g., ASR, NLU, command processing, etc.) as well asother operations as described herein. A single server 120 may performall speech processing or multiple servers 120 may combine to perform allspeech processing. Further, the server(s) 120 may execute certaincommands, such as answering spoken utterances of users 5 and operatingother devices (e.g., light switches, appliances, etc.). In addition,certain speech detection or command execution functions may be performedby the speech-controlled device 110. Further, the system 100 may be incommunication with external data sources, such as a knowledge base,external service provider devices (e.g., application servers 125), orthe like.

As shown in FIG. 1 , a speech-controlled device 110 may capture audio 11including a spoken utterance of a user 5 via one or more microphones ofthe speech-controlled device 110. The speech-controlled device 110determines audio data corresponding to the captured audio 11, and sendsthe audio data to the server(s) 120 for processing.

The server(s) 120 receives (130) the audio data from thespeech-controlled device 110. The server(s) 120 performs (132) speechprocessing on the audio data to determine a user command represented inthe audio data. This may include performing ASR on the input audio datato obtain text data as well as performing NLU operations on the textdata to identify an intent corresponding to the text data.

The server(s) 120 also identifies (134) a user that spoke the utterancerepresented in the audio data. User recognition may involve determiningaudio characteristics of the spoken utterance, and comparing such tostored audio characteristics of users of the speech-controlled device110 from which the audio data was received by the server(s) 120. Otherdata described herein may also be used to recognize the user that spokethe utterance as described below with regard to user recognitioncomponent 295.

The server(s) 120 may access (136) a profile associated with the user,and therein determine (138) non-default content to be output for theuser command. This may include customized content that the user haspreviously indicated should be output in response to the particularspoken utterance. The profile may include data corresponding to usercommands executable by the system 100. The data corresponding to a givenuser command may be associated with data indicating non-default, userindicated content to be output in addition to default content for theuser command. Thus, it should be appreciated that the profile mayinclude data specific to user commands wherein non-default contentshould be output. The profile need not include data pertaining to everyuser command executable by the system 100.

The server(s) 120 may then determine (140) a content source storing orhaving access to the non-default content. The content source may beindicated by the user. For example, if the non-default content isweather related, the user may indicate which weather service the userdesires the non-default content to originate from. Alternatively, thecontent source may be determined without user indication. The contentsource may be a first party (1P) application (such as a core domaincontrolled or managed by the server(s) 120). Alternatively, the contentsource may be a third party (3P) application (such as an add-on domain,e.g., a “skill,” managed by an application server(s) 125 incommunication with the server(s) 120 but not controlled or managed bythe server(s) 120).

A “skill” may correspond to a domain and may be software running on aserver or device akin to an application. That is, a skill may enable aserver(s) 120/125 to execute specific functionality in order to providedata or produce some other output called for by a user. The system maybe configured with more than one skill. For example a weather serviceskill may enable the server(s) 120 to execute a command with respect toa weather service server 125, a car service skill may enable theserver(s) 120 to execute a command with respect to a taxi service server125, an order pizza skill may enable the server(s) 120 to execute acommand with respect to a restaurant server 125, etc.

The server receives (142) the non-default content from the determinedcontent source, as well as receives (144) default content to be outputfor the user command from a content source(s). As with the contentsource of the non-default content, the content source of the defaultcontent may be a 1P application or a 3P application. The server(s) 120may then generate (146) customized output data using the non-defaultcontent and default content, for example by combining the non-defaultcontent and default content into the customized output data.

In an example, both the default and non-default content may be receivedas text data from the content source(s). In such an example, theserver(s) 120 may perform (148) TTS on the customized output data togenerate output audio data. In another example, both the default andnon-default content may be received as audio data from the contentsource(s). In this example, the server(s) 120 may concatenate thereceived portions of audio data to generate the output audio data. In afurther example, one of the default and non-default content may bereceived as audio data while the other content is received as text data.In this example, the server(s) 120 may perform TTS on the received textdata to generate audio data, and thereafter concatenate the receivedaudio data with the generated audio data to generated the output audiodata.

The server(s) 120 sends (150) the output audio data to thespeech-controlled device 110 (or another device indicated in the profileof the user 5). The speech-controlled device 110 (or other device)outputs audio corresponding to the output audio data to the user 5.

As described, the server(s) 120 may compile the default and non-defaultcontent, transform such into outputtable data, and then send such datato the speech-controlled device 110 for output to the user 5. It shouldalso be appreciated that the content source may send the default and/ornon-default content directly to the speech-controlled device 110. Such asituation may occur if both the default and non-default content arestored or accessed by the same content source.

As also described above, the server(s) 120 receives (130) audio datafrom the speech-controlled device 110 and performs (132) speechprocessing on the audio data to determine the user command. Such speechprocessing may include performing ASR on the audio data to generate textdata, and performing NLU on the text data to determine the user command.It should thus also be appreciated that, rather than determine the usercommand from received audio data, the server(s) 120 may determine theuser command from received text data. For example, a user may type textinto an application executed by a computing device such as a smartphone, tablet, or the like. The computing device may generate text datafrom the input text, and send the text data to the server(s) 120. Theserver(s) 120 may then perform NLU on the received text data todetermine the user command.

The system 100 of FIG. 1 may operate using various speech processingcomponents as described in FIG. 2 . FIG. 2 is a conceptual diagram ofhow a spoken utterance is processed. The various components illustratedmay be located on a same or different physical devices. Communicationbetween various components illustrated in FIG. 2 may occur directly oracross a network(s) 199. An audio capture component, such as amicrophone of the device 110 (or other device), captures input audio 11corresponding to a spoken utterance. The device 110, using a wakeworddetection component 234, then processes the audio, or audio datacorresponding to the audio (such as feature vectors), to determine if akeyword (such as a wakeword) is detected in the audio. Followingdetection of a wakeword, the device 110 sends audio data 111,corresponding to the utterance, to a server(s) 120 that includes an ASRcomponent 250. The audio data 111 may be output from an acoustic frontend (AFE) 220 located on the device 110 prior to transmission.Alternatively, the audio data 111 may be in a different form forprocessing by a remote AFE 220, such as the AFE 220 located with the ASRcomponent 250.

Upon receipt by the server(s) 120, an ASR component 250 may convert theaudio data 111 into text data. The ASR component 250 transcribes theaudio data 111 into text data representing words of speech contained inthe audio data 111. The text data may then be used by other componentsfor various purposes, such as executing system commands, inputting data,etc. A spoken utterance in the audio data 111 is input to a processorconfigured to perform ASR, which then interprets the spoken utterancebased on a similarity between the spoken utterance and pre-establishedlanguage models 254 stored in an ASR model knowledge base (i.e., ASRmodel storage 252). For example, the ASR component 250 may compare theaudio data 111 with models for sounds (e.g., subword units or phonemes)and sequences of sounds to identify words that match the sequence ofsounds spoken in the spoken utterance of the audio data 111.

The different ways a spoken utterance may be interpreted (i.e., thedifferent hypotheses) may each be assigned a probability or a confidencescore representing a likelihood that a particular set of words matchesthose spoken in the spoken utterance. The confidence score may be basedon a number of factors including, for example, a similarity of the soundin the spoken utterance to models for language sounds (e.g., an acousticmodel 253 stored in the ASR model storage 252), and a likelihood that aparticular word that matches the sound would be included in the sentenceat the specific location (e.g., using a language model 254 stored in theASR model storage 252). Thus, each potential textual interpretation ofthe spoken utterance (i.e., hypothesis) is associated with a confidencescore. Based on the considered factors and the assigned confidencescore, the ASR component 250 outputs text data representing the mostlikely text recognized in the audio data 111. The ASR component 250 mayalso output multiple hypotheses in the form of a lattice or an N-bestlist with each hypothesis corresponding to a confidence score or otherscore (e.g., such as probability scores, etc.).

The device(s) including the ASR component 250 may include an AFE 220 anda speech recognition engine 258. The AFE 220 may transform raw audiodata, captured by the microphone of the device 110, into audio data forprocessing by the speech recognition engine 258. The speech recognitionengine 258 compares the transformed, speech recognition audio data withacoustic models 253, language models 254, and other data models andinformation for recognizing the speech conveyed in the audio data 111.

The speech recognition engine 258 may process data output from the AFE220 with reference to information stored in the ASR model storage 252.Alternatively, post front-end processed data (e.g., feature vectors) maybe received by the device(s) executing ASR processing from anothersource besides the internal AFE 220. For example, the device 110 mayprocess audio data 111 into feature vectors (e.g., using an on-deviceAFE 220) and transmit that information to the server(s) 120 across thenetwork(s) 199 for ASR processing. Feature vectors may arrive at theserver(s) 120 encoded, in which case they may be decoded prior toprocessing by the processor executing the speech recognition engine 258.

The speech recognition engine 258 attempts to match received featurevectors to language phonemes and words as known in the stored acousticmodels 253 and language models 254. The speech recognition engine 258computes recognition scores for the feature vectors based on acousticinformation and language information. The acoustic information is usedto calculate an acoustic score representing a likelihood that theintended sound represented by a group of feature vectors matches alanguage phoneme. The language information is used to adjust theacoustic score by considering what sounds and/or words are used incontext with each other, thereby improving a likelihood that the ASRcomponent 250 will output text data that makes sense grammatically.

The speech recognition engine 258 may use a number of techniques tomatch feature vectors to phonemes, for example using Hidden MarkovModels (HMIs) to determine probabilities that feature vectors may matchphonemes. Sounds received may be represented as paths between states ofthe HMM and multiple paths may represent multiple possible text matchesfor the same sound.

Following ASR processing, the ASR results may be sent by the speechrecognition engine 258 to other processing components, which may belocal to the device(s) performing ASR and/or distributed across thenetwork(s) 199. For example, ASR results in the form of a single textualrepresentation of the speech, an N-best list including multiplehypotheses and respective scores, lattice, etc. may be sent to a server,such as the server(s) 120, for natural language understanding (NLU)processing, such as conversion of the text data into commands forexecution, either by the device 110, by the server(s) 120, or by anotherdevice (e.g., a server running a search engine, etc.)

The device(s) performing NLU processing (e.g., the server(s) 120) mayinclude various components, including potentially dedicatedprocessor(s), memory, storage, etc. As shown in FIG. 2 , a NLU component260 may include a recognizer 263 that includes a named entityrecognition (NER) component 262 which is used to identify portions oftext data that correspond to a named entity that may be recognizable bythe system. A downstream process called named entity resolution actuallylinks a text portion to an actual specific entity known to the system.To perform named entity resolution, the system may utilize gazetteerinformation 284 stored in an entity library storage 282. The gazetteerinformation 284 may be used for entity resolution, for example matchingASR results (i.e., text data) with different entities (such as songtitles, contact names, etc.). Gazetteers 284 may be linked to users (forexample a particular gazetteer 284 may be associated with a specificuser's music collection), may be linked to certain domains (such asshopping), or may be organized in a variety of other ways. The NERcomponent 262 (or other component) may also determine whether a wordrefers to an entity that is not explicitly mentioned in the utterancetext, for example “him,” “her,” “it” or other anaphora, exophora or thelike.

Generally, the NLU component 260 takes text data and attempts to make asemantic interpretation of the text represented therein. That is, theNLU component 260 determines the meaning behind the text based on theindividual words and then implements that meaning. NLU processinginterprets a text string to derive an intent or a desired action fromthe user as well as the pertinent pieces of information in the text thatallow a device (e.g., the device 110) to complete that action. Forexample, if the ASR component 250 processes a spoken utterance andoutputs text data including the text “call mom,” the NLU component 260may determine that the user intended to activate a telephone in his/herdevice and to initiate a call with a contact matching the entity “mom”(which may involve a downstream command processor 290 linked with atelephone application).

The NLU component 260 may process several textual inputs related to thesame utterance. For example, if the ASR component 250 outputs N textsegments (as part of an N-best list), the NLU component 260 may processall N outputs to obtain NLU results.

The NLU component 260 may be configured to parse and tag to annotatetext as part of NLU processing. For example, for the text “call mom,”“call” may be tagged as a command (to execute a phone call) and “mom”may be tagged as a specific entity and target of the command. Moreover,the telephone number for the entity corresponding to “mom” stored in acontact list may be included in the annotated text. Further, the NLUcomponent 260 may be used to provide answer data in response to queries,for example using a NLU knowledge base 273.

To correctly perform NLU processing, the NLU component 260 may beconfigured to determine a “domain” of the utterance so as to determineand narrow down which services offered by an endpoint device (e.g., theserver(s) 120 or the device 110) may be relevant. For example, anendpoint device may offer services relating to interactions with atelephone service, a contact list service, a calendar/schedulingservice, a music player service, etc. Words in a single portion of textdata input into the NLU component 260 may implicate more than oneservice, and some services may be functionally linked (e.g., both atelephone service and a calendar service may utilize data from thecontact list).

The NER component 262 receives text data and attempts to identifyrelevant grammars and lexical information that may be used to construemeaning. To do so, the NLU component 260 may begin by identifyingpotential domains that may relate to the received text data. The NLUstorage 273 includes a database of device domains (274 a-274 n)identifying domains associated with specific devices. For example, thedevice 110 may be associated with domains for music, telephony,calendaring, contact lists, and device-specific communications. Inaddition, the entity library 282 may include database entries aboutspecific services on a specific device, either indexed by Device ID,Speaker ID, Household ID, or some other indicator.

In NLU processing, a domain may represent a discrete set of activitieshaving a common theme, such as “shopping,” “music,” “calendaring,” etc.As such, each domain may be associated with a particular recognizer 263,language model and/or grammar database (276 a-276 n), a particular setof intents/actions (278 a-278 n), and a particular personalized lexicon(286). Each gazetteer (284 a-284 n) may include domain-indexed lexicalinformation associated with a particular user and/or device. Forexample, the Gazetteer A (284 a) includes domain-index lexicalinformation 286 aa to 286 an. A user's music-domain lexical informationmay include album titles, artist names, and song names, for example,whereas a user's contact-list lexical information may include the namesof contacts. Since every user's music collection and contact list ispresumably different, this personalized information improves entityresolution.

In traditional NLU processing, text data may be processed applying therules, models, and information applicable to each identified domain. Forexample, if text data potentially implicates both communications andmusic, the text data may, substantially in parallel, be NLU processedusing the grammar models and lexical information for communications, andprocessed using the grammar models and lexical information for music.The responses based on the text data produced by each set of models isscored (discussed further below), with the overall highest ranked resultfrom all applied domains being ordinarily selected to be the correctresult.

An intent classification (IC) component 264 parses the text data todetermine an intent(s) for each identified domain, where the intentcorresponds to the action to be performed that is responsive to thespoken utterance represented in the text data. Each domain is associatedwith a database (278 a-278 n) of words linked to intents. For example, amusic intent database may link words and phrases such as “quiet,”“volume off,” and “mute” to a “mute” intent. The IC component 264identifies potential intents for each identified domain by comparingwords in the text data to the words and phrases in the intents database278. The determination of an intent by the IC component 264 may beperformed using a set of rules or templates that are processed againstthe incoming text data to identify a matching intent.

In order to generate a particular interpreted response, the NERcomponent 262 applies the grammar models and lexical informationassociated with the respective domain to recognize a mention of one ormore entities in the text of the text data. In this manner, the NERcomponent 262 identifies words corresponding to “slots” (i.e.,particular words in text data that may be used to fill various datafields used to execute commands) that may be needed for later commandprocessing. Depending on the complexity of the NER component 262, theNER component 262 may also label each slot with a type of varying levelsof specificity (such as noun, place, city, artist name, song name, orthe like). Each grammar model 276 includes the names of entities (i.e.,nouns) commonly found in speech about the particular domain (i.e.,generic terms). For example, a grammar model associated with theshopping domain may include a database of words commonly used whenpeople discuss shopping. In contrast, the lexical information 286 fromthe gazetteer 284 is personalized to the user(s) and/or the device.

The intents identified by the IC component 264 are linked todomain-specific grammar frameworks (included in 276) with “slots” ordata fields to be filled. Each slot/data field corresponds to a portionof the text data that the system believes corresponds to an entity. Forexample, if “play music” is an identified intent, a grammar framework(s)276 may correspond to sentence structures such as “Play {Artist Name},”“Play {Album Name},” “Play {Song name},” “Play {Song name} by {ArtistName},” etc. However, to make resolution more flexible, these frameworksmay not be structured as sentences, but rather based on associatingslots with grammatical tags.

For example, the NER component 262 may parse the text data to identifywords as subject, object, verb, preposition, etc., based on grammarrules and/or models, prior to recognizing named entities. The identifiedverb may be used by the IC component 264 to identify intent, which isthen used by the NER component 262 to identify frameworks. A frameworkfor an intent of “play” may specify a list of slots/fields applicable toplay the identified “object” and any object modifier (e.g., aprepositional phrase), such as {Artist Name}, {Album Name}, {Song name},etc. The NER component 262 then searches the corresponding fields in thedomain-specific and personalized lexicon(s), attempting to match wordsand phrases in the text data tagged as a grammatical object or objectmodifier with those identified in the database(s).

This process includes semantic tagging, which is the labeling of a wordor combination of words according to their type/semantic meaning.Parsing may be performed using heuristic grammar rules, or the NERcomponent 262 may be constructed using techniques such as HMMs, maximumentropy models, log linear models, conditional random fields (CRF), andthe like.

For instance, text data corresponding to “play mother's little helper bythe rolling stones” may be parsed and tagged as {Verb}: “Play,”{Object}: “mother's little helper,” {Object Preposition}: “by,” and{Object Modifier}: “the rolling stones.” At this point in the process,“Play” is identified as a verb based on a word database associated withthe music domain, which the IC component 264 will determine correspondsto the “play music” intent. At this stage, no determination has beenmade as to the meaning of “mother's little helper” and “the rollingstones,” but based on grammar rules and models, it is determined thatthe text of these phrases relate to the grammatical object (i.e.,entity) of the text data.

The frameworks linked to the intent are then used to determine whatdatabase fields should be searched to determine the meaning of thesephrases, such as searching a user's gazetteer for similarity with theframework slots. So a framework for “play music intent” might indicateto attempt to resolve the identified object based on {Artist Name},{Album Name}, and {Song name}, and another framework for the same intentmight indicate to attempt to resolve the object modifier based on{Artist Name}, and resolve the object based on {Album Name} and {SongName} linked to the identified {Artist Name}. If the search of thegazetteer does not resolve the slot/field using gazetteer information,the NER component 262 may search a database of generic words associatedwith the domain (in the knowledge base 273). So for instance, if thetext data corresponded to “play songs by the rolling stones,” afterfailing to determine an album name or song name called “songs” by “therolling stones,” the NER component 262 may search the domain vocabularyfor the word “songs.” In the alternative, generic words may be checkedbefore the gazetteer information, or both may be tried, potentiallyproducing two different results.

The results of NLU processing may be tagged to attribute meaning to thetext data. So, for instance, “play mother's little helper by the rollingstones” might produce a result of: {domain} Music, {intent} Play Music,{artist name} “rolling stones,” {media type} SONG, and {song title}“mother's little helper.” As another example, “play songs by the rollingstones” might produce: {domain} Music, {intent} Play Music, {artistname} “rolling stones,” and {media type} SONG.

Data output from the NLU processing (which may include tagged text data,commands, etc.) may then be sent to a command processor 290, which maybe located on a same or separate server 120 as part of system 100. Thesystem 100 may include more than one command processor 290, and thedestination command processor 290 may be determined based on the NLUoutput data. For example, if the NLU output data includes a command toplay music, the destination command processor 290 may be a music playingapplication, such as one located on the device 110 or in a music playingappliance, configured to execute a music playing command. If the NLUoutput data includes a search utterance (e.g., requesting the return ofsearch results), the command processor 290 selected may include a searchengine processor, such as one located on a search server, configured toexecute a search command and determine search results, which may includeoutput text data to be processed by a TTS engine and output from adevice as synthesized speech.

If the NLU output data includes a command to obtain content from anothersource, the command processor 290 or other component, through anapplication program interface (API), may send a request for such contentto an appropriate application server 125 or other device. Theapplication server 125 may send the content, for example audio data, tothe command processor 290 or other component. In certain instances, theoutput content sent from the application server 125 may include a linkthat may be sent from the server(s) 120 to the device 110 so that thedevice 110 may use the link to access the output content desired by theuser. In this case, the output content data may be sent from theapplication server 125 through the server(s) 120 to the device 110 ordirectly from the application server 125 to the device 110 (or someother destination appropriate to the command). In certain instances, theoutput content data may be audio data (such as music, a prerecordedreading of an audio book, etc.) and thus may be output through a speakerof the device 110. In other instances, the output content data may betext data (either generated by the application server 125 or by acomponent of the server(s) 120) that needs to be converted into audiodata prior to being output to a user by the device 110.

Various machine learning techniques may be used to train and operatemodels to perform various steps described above, such as ASR functions,NLU functions, etc. Such machine learning techniques may include, forexample, neural networks (such as deep neural networks and/or recurrentneural networks), inference engines, trained classifiers, etc. Examplesof trained classifiers include Support Vector Machines (SVMs), neuralnetworks, decision trees, AdaBoost (short for “Adaptive Boosting”)combined with decision trees, and random forests. Focusing on SVM as anexample, SVM is a supervised learning model with associated learningalgorithms that analyze data and recognize patterns in the data, andwhich are commonly used for classification and regression analysis.Given a set of training examples, each marked as belonging to one of twocategories, an SVM training algorithm builds a model that assigns newexamples into one category or the other, making it a non-probabilisticbinary linear classifier. More complex SVM models may be built with thetraining set identifying more than two categories, with the SVMdetermining which category is most similar to input data. An SVM modelmay be mapped so that the examples of the separate categories aredivided by clear gaps. New examples are then mapped into that same spaceand predicted to belong to a category based on which side of the gapsthey fall on. Classifiers may issue a “score” indicating which categorythe data most closely matches. The score may provide an indication ofhow closely the data matches the category.

In order to apply machine learning techniques, machine learningprocesses themselves need to be trained. Training a machine learningcomponent requires establishing a “ground truth” for the trainingexamples. In machine learning, the term “ground truth” refers to theaccuracy of a training set's classification for supervised learningtechniques. Various techniques may be used to train the models includingbackpropagation, statistical learning, supervised learning,semi-supervised learning, stochastic learning, or other knowntechniques.

The server(s) 120 may further include a user recognition component 295.The user recognition component 295 may be configured to perform userauthentication as well as user verification. The user recognitioncomponent 295 may authenticate and verify a user using image datacaptured by a user device, biometric data (e.g., fingerprint data),previously stored audio data containing speech of a user, etc. Forexample, the user recognition component 295 may authenticate and/orverify a user by comparing speech characteristics in the received audiodata 111 to speech characteristics of user stored in a profile of thedevice 110.

To convert text data into output audio data including speech, the system100 may use a text-to-speech (TTS) component 314 illustrated in FIG. 3 .The TTS component 314 may receive text data (from the command processor290, application server(s) 125, or other component) so the TTS component314 may synthesize speech corresponding to the text data. Speech may besynthesized by the TTS component 314 as described below with respect toFIG. 3 .

The TTS component 314 includes a TTS front end (TTSFE) 316, a speechsynthesis engine 318, and a TTS storage 320. The TTSFE 316 transformsinput text data (e.g., from the command processor 290) into a symboliclinguistic representation for processing by the speech synthesis engine318. The TTSFE 316 may also process tags or other data input to the TTScomponent 314 that indicate how specific words should be pronounced. Thespeech synthesis engine 318 compares the annotated phonetic units andinformation stored in the TTS storage 320 for converting the input textdata into speech (i.e., audio data). The TTSFE 316 and the speechsynthesis engine 318 may include their own controller(s)/processor(s)and memory, or they may use the controller/processor and memory of theserver(s) 120, the speech-controlled device 110, or another device, forexample. Similarly, the instructions for operating the TTSFE 316 and thespeech synthesis engine 318 may be located within the TTS component 314,within the memory and/or storage of the server(s) 120, thespeech-controlled device 110, or within an external device.

Text data input into the TTS component 314 may be sent to the TTSFE 316for processing. The TTSFE 316 may include modules for performing textnormalization, linguistic analysis, and linguistic prosody generation.During text normalization, the TTSFE 316 processes the input text dataand generates standard/normalized text data, converting such things asnumbers, abbreviations (e.g., Apt., St., etc.), and symbols ($, %, etc.)into the equivalent of written out words.

During linguistic analysis the TTSFE 316 analyzes language in thenormalized text data to generate a sequence of phonetic unitscorresponding to the text data. This process may be referred to asphonetic transcription. Phonetic units include symbolic representationsof sound units to be eventually combined and output by the system 100 asspeech. Various sound units may be used for dividing text data forpurposes of speech synthesis. The TTS component 314 may process speechbased on phonemes (i.e., individual sounds), half-phonemes, di-phones(i.e., the last half of one phoneme coupled with the first half of anadjacent phoneme), bi-phones (i.e., two consecutive phonemes),syllables, words, phrases, sentences, or other units. Each word may bemapped to one or more phonetic units. Such mapping may be performedusing a language dictionary stored by the system 100, for example in theTTS storage 320. The linguistic analysis performed by the TTSFE 316 mayalso identify different grammatical components such as prefixes,suffixes, phrases, punctuation, syntactic boundaries, or the like. Suchgrammatical components may be used by the TTS component 314 to craft anatural sounding audio waveform output. The language dictionary may alsoinclude letter-to-sound rules and other tools that may be used topronounce previously unidentified words or letter combinations that maybe encountered by the TTS component 314. Generally, the more informationincluded in the language dictionary, the higher quality the speechoutput.

Based on the linguistic analysis, the TTSFE 316 may then performlinguistic prosody generation where the phonetic units are annotatedwith desired prosodic characteristics, also called acoustic features,which indicate how the desired phonetic units are to be pronounced inthe eventual output speech. During this stage, the TTSFE 316 mayconsider and incorporate any prosodic annotations that accompanied thetext data input to the TTS component 314. Such acoustic features mayinclude pitch, energy, duration, and the like. Application of acousticfeatures may be based on prosodic models available to the TTS component314. Such prosodic models indicate how specific phonetic units are to bepronounced in certain circumstances. A prosodic model may consider, forexample, a phoneme's position in a syllable, a syllable's position in aword, a word's position in a sentence or phrase, neighboring phoneticunits, etc. As with the language dictionary, a prosodic model with moreinformation may result in higher quality speech output than prosodicmodels with less information.

The output of the TTSFE 316, referred to as a symbolic linguisticrepresentation, may include a sequence of phonetic units annotated withprosodic characteristics. This symbolic linguistic representation may besent to the speech synthesis engine 318, also known as a synthesizer,for conversion into an audio waveform of speech for output to an audiooutput device and eventually to a user. The speech synthesis engine 318may be configured to convert the input text data into high-qualitynatural-sounding speech in an efficient manner. Such high-quality speechmay be configured to sound as much like a human speaker as possible, ormay be configured to be understandable to a listener without attempts tomimic a precise human voice.

The speech synthesis engine 318 may perform speech synthesis using oneor more different methods. In one method of synthesis called unitselection, a unit selection engine 330 matches the symbolic linguisticrepresentation created by the TTSFE 316 against a database of recordedspeech, such as a database of a voice corpus. The unit selection engine330 matches the symbolic linguistic representation against spoken audiounits in the database. Matching units are selected and concatenatedtogether to form a speech output. Each unit includes an audio waveformcorresponding with a phonetic unit, such as a short .wav file of thespecific sound, along with a description of the various acousticfeatures associated with the .wav file (e.g., its pitch, energy, etc.),as well as other information, such as where the phonetic unit appears ina word, sentence, or phrase, neighboring phonetic units, etc. Using allthe information in the unit database, the unit selection engine 330 maymatch units to the input text data to create a natural soundingwaveform. The unit database may include multiple examples of phoneticunits to provide the system 100 with many different options forconcatenating units into speech. One benefit of unit selection is that,depending on the size of the database, a natural sounding speech outputmay be generated. As described above, the larger the unit database ofthe voice corpus, the more likely the system 100 will be able toconstruct natural sounding speech.

In another method of synthesis called parametric synthesis, parameterssuch as frequency, volume, and noise are varied by a parametricsynthesis engine 332, a digital signal processor, or other audiogeneration device to create an artificial speech waveform output.Parametric synthesis uses a computerized voice generator, sometimescalled a vocoder. Parametric synthesis may use an acoustic model andvarious statistical techniques to match a symbolic linguisticrepresentation with desired output speech parameters. Parametricsynthesis may include the ability to be accurate at high processingspeeds, as well as the ability to process speech without large databasesassociated with unit selection, but also typically produces an outputspeech quality that may not match that of unit selection. Unit selectionand parametric techniques may be performed individually or combinedtogether and/or combined with other synthesis techniques to producespeech audio data output.

Parametric speech synthesis may be performed as follows. The TTScomponent 314 may include an acoustic model, or other models, which mayconvert a symbolic linguistic representation into a synthetic acousticwaveform of the input text data based on audio signal manipulation. Theacoustic model includes rules which may be used by the parametricsynthesis engine 332 to assign specific audio waveform parameters toinput phonetic units and/or prosodic annotations. The rules may be usedto calculate a score representing a likelihood that a particular audiooutput parameter(s) (e.g., frequency, volume, etc.) corresponds to theportion of the input symbolic linguistic representation received fromthe TTSFE 316.

The parametric synthesis engine 332 may use a number of techniques tomatch speech to be synthesized with input phonetic units and/or prosodicannotations. One common technique is using HMMs. HMMs may be used todetermine probabilities that audio output should match textual input.HMMs may be used to translate from parameters from the linguistic andacoustic space to parameters to be used by a vocoder (i.e., the digitalvoice encoder) to artificially synthesize the desired speech. UsingHMMs, a number of states are presented, in which the states togetherrepresent one or more potential acoustic parameters to be output to thevocoder and each state is associated with a model, such as a Gaussianmixture model. Transitions between states may also have an associatedprobability, representing a likelihood that a current state may bereached from a previous state. Sounds to be output may be represented aspaths between states of the HMM and multiple paths may representmultiple possible audio matches for the same input text data. Eachportion of text data may be represented by multiple potential statescorresponding to different known pronunciations of phonemes and theirparts (e.g., phoneme identity, stress, accent, position, etc.). Aninitial determination of a probability of a potential phoneme may beassociated with one state. As new text data is processed by the speechsynthesis engine 318, the state may change or stay the same, based onprocessing of the new text data. For example, the pronunciation of apreviously processed word might change based on later processed words. AViterbi algorithm may be used to find the most likely sequence of statesbased on the processed text data. The HMMs may generate speech inparametrized form including parameters such as fundamental frequency(f0), noise envelope, spectral envelope, etc. that are translated by avocoder into audio segments. The output parameters may be configured forparticular vocoders such as a STRAIGHT vocoder, TANDEM-STRAIGHT vocoder,harmonic plus noise (HNM) based vocoders, code-excited linear prediction(CELP) vocoders, GlottHMM vocoders, harmonic/stochastic model (HSM)vocoders, or others.

For example, to create the customized speech output of the system 100,the system 100 may be configured with multiple voice inventories 378(stored in TTS voice unit storage 372), where each unit database isconfigured with a different “voice.” Such voice inventories may also belinked to user accounts. For example, one voice corpus may be stored tobe used to synthesize whispered speech (or speech approximatingwhispered speech), another may be stored to be used to synthesizeexcited speech (or speech approximating excited speech), and so on. Tocreate the different voice corpuses, a multitude of TTS trainingutterances may be spoken by an individual and recorded by the system100. The TTS training utterances used to train a TTS voice corpus may bedifferent from the training utterances used to train an ASR system. Theaudio associated with the TTS training utterances may then be split intosmall audio segments and stored as part of a voice corpus. Theindividual speaking the TTS training utterances may speak in differentvoice qualities to create the customized voice corpuses, for example theindividual may whisper the training utterances, say them in an excitedvoice, and so on. Thus, the audio of each customized voice corpus maymatch a desired speech quality. The customized voice inventory 378 maythen be used during runtime to perform unit selection to synthesizespeech.

As an alternative to customized voice corpuses or customized parametric“voices,” one or more filters may be used to alter traditional TTSoutput to match a desired speech quality (e.g., whisper, shout, etc.).For example, the TTS component 314 may synthesize speech as normal, butthe system 100, either as part of the TTS component 314 or otherwise,may apply a filter to make the synthesized speech take on the desiredspeech quality. In this manner a traditional TTS output may be alteredto take on the desired speech quality.

During runtime the TTS component 314 may receive text data for speechsynthesis along with an indicator for a desired speech quality of theoutput speech. The TTS component 314 may then select a voice matchingthe speech quality, either for unit selection or parametric synthesis,and synthesize speech using the received text data and speech qualityindicator.

FIG. 4 illustrates a user profile storage 402 that includes dataregarding user accounts 404 as described herein. The user profilestorage 402 may be located proximate to the server(s) 120, or mayotherwise be in communication with various components, for example overthe network(s) 199. The user profile storage 402 may include a varietyof information related to individual users, accounts, etc. that interactwith the system 100. For illustration, as shown in FIG. 4 , the userprofile storage 402 may include data regarding the devices associatedwith particular individual user accounts 404. In an example, the userprofile storage 402 is a cloud-based storage. Each user profile 404 mayinclude data such as device identifier (ID) data, name of device data,and location of device data for different devices. In addition, eachuser profile 404 may include user settings, preferences, permissions,etc. with respect to certain domains including the format of output datafrom a domain as well as what customized data may be requested forcertain intents to be executed by the domain. For example, a particularuser profile 404 may indicate that output data related to a productinformation request to a shopping core domain should include outputportions corresponding to product description and price, output data forweather information from a weather core domain should include portionscorresponding to temperature, wind speed, and air quality index, etc. Ifdesired output content is not stored by or otherwise directly availableto the server(s) 120, the application server(s) 125 storing or havingaccess to the content may be indicated in the user profile 404 as well.For example, if the server(s) 120 does not store wind speed content, anapplication server(s) 125 (for example, corresponding to an add-ondomain) storing or having access to wind speed content may be indicatedin the user profile 404. Each user profile 404 may additionally includeuser affinity data, such as occupation of the user, hobbies of the user,etc.

FIG. 5A illustrates a default weather core domain output data formataccording to the present disclosure. As illustrated, the default weathercore domain output data format is configured with an intro portion thatindicates the location of the device that captured the user utterance,as well as a temperature portion that includes the high and lowtemperatures of the device's location for that day. FIG. 5B illustratesa weather core domain output data format altered based on preferencescontained in a user profile. As illustrated in FIG. 5B, the alteredweather core domain output data format is configured with the intro andtemperature portions of the default output data format, as well as anair index portion and an average wind speed portion. Otherconfigurations are also possible.

FIGS. 6A through 6C illustrate the processing of a user command and thegeneration of responsive content using either a default or a temporaryoutput data format. A temporary output data format is one that theserver(s) 120 deletes from memory after output audio data is generatedtherefrom. The speech-controlled device 110 receives (602) audiocorresponding to a spoken utterance, and sends (604) input audio datacorresponding thereto to the server(s) 120 for processing.

The server(s) 120, upon receiving the input audio data, performs speechprocessing on the input audio data. Namely, the server(s) 120 mayperform (606) ASR on the input audio data to generate text data, and mayperform (608) NLU on the text data to determine a command represented inthe spoken utterance.

The server(s) 120 may determine (610) a core domain associated with thecommand. For example, if the user command is “what is the weather,” thecore domain associated therewith may be a weather domain. For furtherexample, if the user command is “play Adele's Water Under the Bridge,”the core domain associated therewith may be a music domain. A domain maycorrespond to a type of content that may be output by the system. Typesof content may include, for example, weather content, music content,video content, or the like. The variety and amount of user commands andassociated core domains is system configurable.

The server(s) 120 may also determine (612) a user that spoke theutterance. This may include receiving biometric data a user, image dataincluding a representation of a face of a user, and/or audio dataincluding speech of a user, and comparing such to biometric data, imagedata, and/or audio data associated with users in a profile of the device110 from which the spoken utterance audio data was received by theserver(s) 120.

The server(s) 120 may access (614 illustrated in FIG. 6B) a profile ofthe determined user, and determine (616), in the user profile,non-default portions for the core domain. The server(s) 120 may generate(618) a temporary output data format including default portions (i.e.,portions indicated in the default output data format for the coredomain) as well as non-default portions (i.e., portions associated withthe core domain in the user profile, but not part of the default outputdata format for the core domain).

The server(s) 120 may determine (620) one or more application servers125 storing or having access to content used to fill portions of thetemporary output data format. If more than one application server 125 isdetermined to store or have access to content needed for a singleportion of the temporary output data format, the server(s) 120 maychoose a single application server 125 to receive the content from basedon user preferences or other considerations.

The server(s) 120 may send (622 illustrated in FIG. 6C) a signal to eachapplication server 125 from which content is desired, with each signalrequesting respective content for the temporary output data format. If asingle application server 125 stores or has access to content used tofill multiple portions of the temporary output data format, a singlesignal indicating and requesting all desired content may be sent to theapplication server 125, or separate signals indicating separate portionsof desired content may be sent to the application server 125.

The server(s) 120 receives (624) content data for the temporary outputdata format from one or more application servers 125. The server(s) 120also performs (626 illustrated in FIG. 6C) TTS using the temporaryoutput data format, received content data used to fill default portionsof the temporary output data format, and received content data used tofill non-default portions of the temporary output data format togenerate second output audio data. The content data may be received bythe server(s) 120 as either text data or audio data. The server(s) 120may insert received text data and audio data into appropriate segmentsof the temporary output data format, and perform TTS on the textportions of the filled temporary output data format, but not audioportions of the filled temporary output data format. The server(s) 120sends (628) the second output audio data to the device 110, and thedevice 110 outputs (630) audio corresponding to the second output audiodata.

As indicated above, the system 100 (e.g., the server(s) 120) may createa temporary output data format each time a user speaks a command thatinvokes a core domain for which the user prefers non-default content tobe output. The user may speak a command that invokes a given core domainmultiple times. Thus, rather than repeatedly create a temporary outputdata format each time, the server(s) 120 may create a customized outputdata format including default and non-default portions, and store suchin a profile of the user (as illustrated in FIGS. 7A through 7C). Thecustomized output data format may be generated when the user indicatesthe non-default portions. Alternatively, the customized output dataformat may be generated the first time a user speaks a commandtriggering the core domain after indicating the non-default portions.

The speech-controlled device 110 receives (602 illustrated in FIG. 7A)audio corresponding to a spoken utterance, and sends (604) input audiodata corresponding thereto to the server(s) 120 for processing.

The server(s) 120, upon receiving the input audio data, performs speechprocessing on the input audio data. Namely, the server(s) 120 mayperform (606) ASR on the input audio data to generate text data, and mayperform (608) NLU on the text data to determine a command represented inthe spoken utterance.

The server(s) 120 may determine (610) a core domain associated with thecommand. For example, if the user command is “what is the weather,” thecore domain associated therewith may be a weather domain. For furtherexample, if the user command is “play Adele's Water Under the Bridge,”the core domain associated therewith may be a music domain. A domain maycorrespond to a type of content that may be output by the system. Typesof content may include, for example, weather content, music content,video content, or the like. The variety and amount of user commands andassociated core domains is system configurable.

The server(s) 120 may also determine (612) a user that spoke theutterance. This may include receiving biometric data a user, image dataincluding a representation of a face of a user, and/or audio dataincluding speech of a user, and comparing such to biometric data, imagedata, and/or audio data associated with users in a profile of the device110 from which the spoken utterance audio data was received by theserver(s) 120.

The server(s) 120 may access (614 illustrated in FIG. 7B) a profile ofthe determined user, and determine (702), in the user profile, acustomized output data format associated with the core domain. Thecustomized output data format may be generated by the server(s) 120 whenthe user indicates non-default portions to be output. The customizedoutput data format may be generated by adding non-default portions tothe default output data format of the core domain.

The server(s) 120 may determine (704) one or more application servers125 storing or having access to content used to fill portions of thecustomized output data format. If more than one application server 125is determined to store or have access to content needed for a singleportion of the customized output data format, the server(s) 120 maychoose a single application server 125 to receive the content from basedon user preferences or other considerations.

The server(s) 120 may send (706) a signal to each application server 125from which content is desired, with each signal requesting respectivecontent for the customized output data format. If a single applicationserver 125 stores or has access to content used to fill multipleportions of the customized output data format, a single signalindicating and requesting all desired content may be sent to theapplication server 125, or separate signals indicating separate portionsof desired content may be sent to the application server 125.

The server(s) 120 receives (708) content data for the customized outputdata format from one or more application servers 125. The server(s) 120also performs (710 illustrated in FIG. 7C) TTS using the customizedoutput data format, received content data used to fill portions of thecustomized output data format corresponding to portions of the defaultoutput data format for the core domain, and received content data usedto fill portions of the customized output data format not included inthe default output data format to generate output audio data. Thecontent data may be received by the server(s) 120 as either text data oraudio data. The server(s) 120 may insert received text data and audiodata into appropriate segments of the customized output data format, andperform TTS on the text portions of the filled customized output dataformat, but not audio portions of the filled customized output dataformat. The server(s) 120 sends (712) the output audio data to thedevice 110, and the device 110 outputs (714) audio corresponding to theoutput audio data.

As described above, the text data processed by the system to ultimatelydetermine output content is generated from audio data corresponding to auser utterance. For example, each time a user enters their house theymay say “how many messages do I have” and the system may response. Itshould also be appreciated that the text data may be generated based onan event. According to the above example, rather than require the userto say “how many messages do I have,” the system may determine when auser enters their house, and may therefrom automatically generate textdata corresponding to “how many messages do I have.” The user mayinstruct the system (e.g., in the user's preferences) that each time theuser enters their house, the user wants the system to tell the user howmany messages the user has. Alternatively, the system may keep a log ofevents and corresponding user commands. After an event occurs with asingle corresponding command over a threshold amount of times, thesystem may automatically configure itself to generate the text datacorresponding to the command with user instruction to do so. The systemmay store event data and corresponding text data in the user's profile.

FIG. 8 illustrates an output data format associated with domain specificslot data. Each domain invoked by an output data format may beassociated with a different 1P or 3P application. Each 1P or 3Papplication may require different data in order to properly determinedata responsive to a user command. The server(s) 120 may determine thedata needed for each domain, and associate such data with the respectivedomain in the output data format as slots/data fields that need to befilled. The slots and/or intents may be represented by indicators ofwhat data is requested from the domain, such as those illustrated inFIG. 8 . For example, a user command of “what is the weather” may invokean output data format including a default portion associated with a coredomain, and a non-default portion corresponding to wind speed fromanother domain. The core domain (Domain 1) may require datacorresponding to the type of user command as well as the type of datarequested from the core domain, and the other domain (Domain 2) mayrequire data corresponding to the type of user command as well as thedata requested from the other domain. Context data may also be passed tothe domain(s). Context data includes, for example, user profile data,user occupation data, user affinity (e.g., user history regardinginteractions with the system) data, and the like. If the output dataformat is associated with a user profile, the server(s) 120 may storeand associate such data in the user profile.

In another example, a user profile may indicate that for intentsinvolving recipes should request the high altitude version of therecipe. As shown in FIG. 8 , a user request for a recipe correspondingto this user profile may append the slot related to altitude to a reciperequest. In another example, a user profile may indicate that requestsfor information be taken from the home country of a user, particularlyif that home country is different from the user's location or devicelocation. Thus, information requests may have appended to them a slotcorresponding to the geographic location of the home country.

Associating the slot data with the domain in the output data formatensures the system will obtain and output proper data to the user evenif speech processing of the user command does not result inidentification of the slots. For example, the server(s) 120 may performASR on audio data to generate text data. The server(s) 120 may thenperform NLU on the text data to determine the user command. In somecases, NLU does not determine slots of data needed by a given domain. Insuch situations, the server(s) 120 may determine such data slots fromthe user's profile.

In certain implementations, a command processor 290 for a first domain(domain 1) may be configured to control a second domain (domain 2). Thatis, domain 1 may specify the data sent to domain 2. For example, domain1 may ingest the data input therein and determine, based on userinformation, that other personalized information may be provided to theuser. In that case, if domain 1 does not have access to the otherpersonalized information, domain 1 may indicate to the system thatdomain 2 should be engaged to obtain the other information.

The system may also be configured to append additional information todefault output information without a user instruction to do so. Forexample, the default output format for a weather domain may includetemperature information. In some instances, the system may appendadditional data to the default output data based on user location. Forexample, if the user is determined to be in Los Angeles, Calif., thesystem may also output air quality information. In another example, ifthe user is determined to be in Colorado, the system may also outputsnowfall information. This type of functionality may be structured asif-then processing. For example, the system may have store data withrespect to various locations specific to a certain domain. For example,the weather domain may be configured to always output temperatureinformation. The weather domain may also be associated with if-then orother logic specific to certain conditions, such as differentinformation to be provided for various locations. For example, theif-then logic for the weather domain may include if the user is in LosAngeles, Calif., then output air quality information; if the user is inColorado, then output snowfall information; if the user is in Arizona,then output heat index information; etc.

In other implementations, the system may be configured to prompt theuser with respect to additional information to be output. For example, aweather domain may be configured to output temperature information. Whena user asks “what is the weather,” the system may determine the user'slocation, and determine a temperature corresponding thereto. Inaddition, the system may determine if-then logic associated with thelocation. Thus, in according to the above example, the system may output“I noticed you are in Colorado. The temperature in Colorado today is 78°F. Would you also like to know the snowfall for today?” In theaforementioned output, the statement “would you also like to know thesnowfall today” may be triggered by if-then logic indicating if the useris in Colorado, the output snowfall information.

Domain developers may specify the extra information or add-ons that maybe triggered and output by a given domain. However, the domainsdevelopers may not set when the additional information or add-ons may betriggered. The system disclosed herein may be configured to enable theadd-ons and/or the add-ons may be enabled by a user.

FIG. 9 is a block diagram conceptually illustrating a user device thatmay be used with the described system. FIG. 10 is a block diagramconceptually illustrating example components of a remote device, such asthe server(s) 120 that may assist with ASR processing, NLU processing,or command processing. Multiple servers 120 may be included in thesystem 100, such as one server 120 for performing ASR, one server 120for performing NLU, etc. In operation, each of these devices (or groupsof devices) may include computer-readable and computer-executableinstructions that reside on the respective device (110/120), as will bediscussed further below.

Each of these devices (110/120) may include one or morecontrollers/processors (904/1004), which may each include a centralprocessing unit (CPU) for processing data and computer-readableinstructions, and a memory (906/1006) for storing data and instructionsof the respective device. The memories (906/1006) may individuallyinclude volatile random access memory (RAM), non-volatile read onlymemory (ROM), non-volatile magnetoresistive (MRAM), and/or other typesof memory. Each device (110/120) may also include a data storagecomponent (908/1008) for storing data andcontroller/processor-executable instructions. Each data storagecomponent (908/1008) may individually include one or more non-volatilestorage types such as magnetic storage, optical storage, solid-statestorage, etc. Each device (110/120) may also be connected to removableor external non-volatile memory and/or storage (such as a removablememory card, memory key drive, networked storage, etc.) throughrespective input/output device interfaces (902/1002).

Computer instructions for operating each device (110/120) and itsvarious components may be executed by the respective device'scontroller(s)/processor(s) (904/1004), using the memory (906/1006) astemporary “working” storage at runtime. A device's computer instructionsmay be stored in a non-transitory manner in non-volatile memory(906/1006), storage (908/1008), or an external device(s). Alternatively,some or all of the executable instructions may be embedded in hardwareor firmware on the respective device in addition to or instead ofsoftware.

Each device (110/120) includes input/output device interfaces(902/1002). A variety of components may be connected through theinput/output device interfaces (902/1002), as will be discussed furtherbelow. Additionally, each device (110/120) may include an address/databus (924/1024) for conveying data among components of the respectivedevice. Each component within a device (110/120) may also be directlyconnected to other components in addition to (or instead of) beingconnected to other components across the bus (924/1024).

Referring to FIG. 9 , the device 110 may include input/output deviceinterfaces 902 that connect to a variety of components such as an audiooutput component such as a speaker 918, a wired headset or a wirelessheadset (not illustrated), or other component capable of outputtingaudio. The device 110 may also include an audio capture component. Theaudio capture component may be, for example, a microphone 920 or arrayof microphones, a wired headset or a wireless headset (not illustrated),etc. The microphone 920 may be configured to capture audio. If an arrayof microphones is included, approximate distance to a sound's point oforigin may be determined by acoustic localization based on time andamplitude differences between sounds captured by different microphonesof the array.

Via antenna(s) 914, the input/output device interfaces 902 may connectto one or more networks 199 via a wireless local area network (WLAN)(such as WiFi) radio, Bluetooth, and/or wireless network radio, such asa radio capable of communication with a wireless communication networksuch as a Long Term Evolution (LTE) network, WiMAX network, 3G network,4G network, 5G network, etc. A wired connection such as Ethernet mayalso be supported. Through the network(s) 199, the system 100 may bedistributed across a networked environment.

The device 110 and/or the server(s) 120 may include the ASR component250. The ASR component 250 in the device 110 may be of limited orextended capabilities. The ASR component 250 may include the languagemodels 254 stored in ASR model storage component 252. If limited speechrecognition is included, the ASR component 250 may be configured toidentify a limited number of words, whereas extended speech recognitionmay be configured to recognize a much larger range of words.

The device 110 and/or the server(s) 120 may include the NLU component260. The NLU component 260 in the device 110 may be of limited orextended capabilities. The NLU component 260 may comprise the nameentity recognition component 262, the intent classification component264, and/or other components. The NLU component 260 may also include astored knowledge base and/or entity library, or those storages may beseparately located.

The device 110 and/or the server(s) 120 may also include the commandprocessor 290 configured to execute commands/functions associated with aspoken utterance as described herein.

The device 110 may further include the wakeword detection component 234as described herein.

The server(s) 120 may further include the user recognition component 295as described herein.

As noted above, multiple devices may be employed in a single speechprocessing system. In such a multi-device system, each of the devicesmay include different components for performing different aspects of thespeech processing. The multiple devices may include overlappingcomponents. The components of the device 110 and the server(s) 120, asillustrated in FIGS. 9 and 10 , are exemplary, and may be located as astand-alone device or may be included, in whole or in part, as acomponent of a larger device or system.

As illustrated in FIG. 11 , multiple devices (110, 110 b-110 e, 120.125) may contain components of the system 100 and the devices may beconnected over a network(s) 199. The network(s) 199 may include a localor private network or may include a wide network such as the Internet.Devices may be connected to the network(s) 199 through either wired orwireless connections. For example, the speech-controlled device 110, asmart phone 110 b, a smart watch 110 c, a tablet computer 110 d, and/ora vehicle 110 e may be connected to the network(s) 199 through awireless service provider, over a WiFi or cellular network connection,or the like. Other devices are included as network-connected supportdevices, such as the server(s) 120, application developer devices (e.g.,the application server(s) 125), or others. The support devices mayconnect to the network(s) 199 through a wired connection or wirelessconnection. Networked devices may capture audio using one-or-morebuilt-in or connected microphones or audio capture devices, withprocessing performed by ASR, NLU, or other components of the same deviceor another device connected via the network(s) 199, such as the ASRcomponent 250, the NLU component 260, etc. of one or more servers 120.

The concepts disclosed herein may be applied within a number ofdifferent devices and computer systems, including, for example,general-purpose computing systems, speech processing systems, anddistributed computing environments.

The above aspects of the present disclosure are meant to beillustrative. They were chosen to explain the principles and applicationof the disclosure and are not intended to be exhaustive or to limit thedisclosure. Many modifications and variations of the disclosed aspectsmay be apparent to those of skill in the art. Persons having ordinaryskill in the field of computers and speech processing should recognizethat components and process steps described herein may beinterchangeable with other components or steps, or combinations ofcomponents or steps, and still achieve the benefits and advantages ofthe present disclosure. Moreover, it should be apparent to one skilledin the art, that the disclosure may be practiced without some or all ofthe specific details and steps disclosed herein.

Aspects of the disclosed system may be implemented as a computer methodor as an article of manufacture such as a memory device ornon-transitory computer readable storage medium. The computer readablestorage medium may be readable by a computer and may compriseinstructions for causing a computer or other device to perform processesdescribed in the present disclosure. The computer readable storagemedium may be implemented by a volatile computer memory, non-volatilecomputer memory, hard drive, solid-state memory, flash drive, removabledisk, and/or other media. In addition, components of one or more of themodules and engines may be implemented as in firmware or hardware, suchas the AFE 220, which comprises, among other things, analog and/ordigital filters (e.g., filters configured as firmware to a digitalsignal processor (DSP)).

Conditional language used herein, such as, among others, “can,” “could,”“might,” “may,” “e.g.,” and the like, unless specifically statedotherwise, or otherwise understood within the context as used, isgenerally intended to convey that certain embodiments include, whileother embodiments do not include, certain features, elements and/orsteps. Thus, such conditional language is not generally intended toimply that features, elements, and/or steps are in any way required forone or more embodiments or that one or more embodiments necessarilyinclude logic for deciding, with or without other input or prompting,whether these features, elements, and/or steps are included or are to beperformed in any particular embodiment. The terms “comprising,”“including,” “having,” and the like are synonymous and are usedinclusively, in an open-ended fashion, and do not exclude additionalelements, features, acts, operations, and so forth. Also, the term “or”is used in its inclusive sense (and not in its exclusive sense) so thatwhen used, for example, to connect a list of elements, the term “or”means one, some, or all of the elements in the list.

Disjunctive language such as the phrase “at least one of X, Y, Z,”unless specifically stated otherwise, is understood with the context asused in general to present that an item, term, etc., may be either X, Y,or Z, or any combination thereof (e.g., X, Y, and/or Z). Thus, suchdisjunctive language is not generally intended to, and should not, implythat certain embodiments require at least one of X, at least one of Y,or at least one of Z to each be present.

As used in this disclosure, the term “a” or “one” may include one ormore items unless specifically stated otherwise. Further, the phrase“based on” is intended to mean “based at least in part on” unlessspecifically stated otherwise.

What is claimed is:
 1. A computer-implemented method, comprising:receiving input data representing a natural language command;determining that output responsive to the natural language command is toinclude first content of a default content type; identifying a profileassociated with the input data; determining that the profile isassociated with a non-default content type; determining, based at leastin part on the profile being associated with the non-default contenttype, that the output is to additionally include second content of thenon-default content type; receiving first data responsive to the naturallanguage command, the first data corresponding to the default contenttype; receiving second data responsive to the natural language command,the second data corresponding to the non-default content type; and basedat least in part on the first data and the second data, causing theoutput to include at least the first content of the default content typeand the second content of the non-default content type.
 2. Thecomputer-implemented method of claim 1, further comprising: determininga category of the natural language command, wherein determining that theoutput is to additionally include the second content of the non-defaultcontent type is based at least in part on the category.
 3. Thecomputer-implemented method of claim 1, further comprising: determininga geographic location corresponding to the input data, whereindetermining that the output is to additionally include the secondcontent of the non-default content type is based at least in part on thegeographic location.
 4. The computer-implemented method of claim 1,further comprising: determining a first skill corresponding to thenon-default content type; and determining a first componentcorresponding to the first skill; wherein the second data is receivedfrom the first component.
 5. The computer-implemented method of claim 4,wherein determining the first skill is based at least in part on theprofile.
 6. The computer-implemented method of claim 4, furthercomprising: determining a second component, different from the firstcomponent, corresponding to the default content type; wherein the firstdata is received from the second component.
 7. The computer-implementedmethod of claim 1, wherein the first data and the second data correspondto a same domain of a natural language processing system.
 8. Thecomputer-implemented method of claim 1, further comprising: generatingan output data format including a first slot to be populated with thefirst content and a second slot to be populated with the second content;and configuring output data corresponding to the output data format, theoutput data including the first data in the first slot and the seconddata in the second slot, wherein causing the output to include at leastthe first content and the second content uses the output data.
 9. Thecomputer-implemented method of claim 8, wherein generating the outputdata format is based at least in part on the profile.
 10. A system,comprising: at least one processor; and at least one memory comprisinginstructions that, when executed by the at least one processor, causethe system to: receive input data representing a natural languagecommand; determine that output responsive to the natural languagecommand is to include first content of a default content type; identifya profile associated with the input data; determine that the profile isassociated with a non-default content type; determine, based at least inpart on the profile being associated with the non-default content type,that the output is to additionally include second content of thenon-default content type; receive first data responsive to the naturallanguage command, the first data corresponding to the default contenttype; receive second data responsive to the natural language command,the second data corresponding to the non-default content type; and basedat least in part on the first data and the second data, cause the outputto include at least the first content of the default content type andthe second content of the non-default content type.
 11. The system ofclaim 10, wherein the at least one memory further comprises instructionsthat, when executed by the at least one processor, further cause thesystem to: determine a category of the natural language command; anddetermine that the output is to additionally include the second contentof the non-default content type based at least in part on the category.12. The system of claim 11, wherein the at least one memory furthercomprises instructions that, when executed by the at least oneprocessor, further cause the system to: determine a geographic locationcorresponding to the input data; and determine that the output is toadditionally include the second content of the non-default content typebased at least in part on the geographic location.
 13. The system ofclaim 10, wherein the at least one memory further comprises instructionsthat, when executed by the at least one processor, further cause thesystem to: determine a first skill corresponding to the non-defaultcontent type; determine a first component corresponding to the firstskill; and receive the first data from the first component.
 14. Thesystem of claim 13, wherein determination of the first skill is based atleast in part on the profile.
 15. The system of claim 14, wherein the atleast one memory further comprises instructions that, when executed bythe at least one processor, further cause the system to: determine asecond component, different from the first component, corresponding tothe default content type; and receive the second data from the secondcomponent.
 16. The system of claim 11, wherein the first data and thesecond data correspond to a same domain of a natural language processingsystem.
 17. The system of claim 11, wherein the at least one memoryfurther comprises instructions that, when executed by the at least oneprocessor, further cause the system to: generate an output data formatincluding a first slot to be populated with the first content and asecond slot to be populated with the second content; configure outputdata corresponding to the output data format, the output data includingthe first data in the first slot and the second data in the second slot;and cause the output to include the first content and the second contentbased at least in part on the output data.
 18. The system of claim 17,wherein generation of the output data format is based at least in parton the profile.